The purpose of the paper is to provide new information on the performance of frontier estimation methods, using data from Italian hotel industry. Quantile regression is also suggested as solution to frontier production function estimation. It is shown that, while the choice of estimation methods among conventional techniques significantly affects the economic analysis, quantile regression provides valuable new information by estimating the whole spectrum of production functions corresponding to different efficiency levels. In addition, the method makes available a coherent framework to analyze the performance of the conventional techiniques. Copyright Springer-Verlag 2004Production function, stochastic frontier model, semiparametric frontier model, quantile regression,

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Stock price dynamics, price barriers, market efficiency,

In this paper we examine the formal implications of international risk sharing among a set of countries in the presence of market frictions and forward-looking behaviour. We show that if frictions prevent consumption to adjust instantaneously to its optimal long run level, consumption streams in the countries belonging to the risk sharing pool change over time according to a dynamic disequilibrium model which can be nested within an error-correcting vector autoregressive process. Econometric methods for testing the restrictions imposed by the theory at both short and long horizons are proposed and discussed. The empirical analysis of a set of core European countries suggest that consumption data do not seem to contrast neither with the existence of risk sharing against permanent income fluctuations and integrated capital markets, nor with a gradual and interrelated process of adjustment towards the equilibrium. The apparent lack of risk sharing in Europe documented in earlier works might depend not only on the misspecification of the short run dynamics of consumption, but also on the relatively low speed of adjustment toward the equilibrium.

Both the Arbitrage Pricing Theory (APT) and the Capital Asset Pricing Model (CAPM) place restrictions of the cross‐sectional variation of conditional expectations of asset returns and of macro indicators. We show that these restrictions imposed on the reference statistical models lead to special cases of the reduced rank regression model. The maximum likelihood problem is solved by canonical correlation analysis. Likelihood ratio tests about the number of factors underlying stock returns are straightforward to calculate, thus allowing discrimination between competing financial theories. Moreover LR tests on the relevance of each macroeconomic indicator within a chosen model can be implemented. Some of the tests are illustrated by an application to Italian stock market data.